Visualizing Amounts in Finance

Most commonly used visualizations to compare amounts include:

1. Bar Charts
2. Diverging Bar Charts    
3. Stacked Bar Charts
4. Grouped Bar Charts (also called Clustered Bar Charts)
5. Animated Bar Charts
6. Dot Plots (also called Cleveland Dot Charts)
7. Bubble Charts
8. Heatmaps

Others include :
9.  Waterfall Charts
10. Polar Area Charts (also called Coxcomb Charts)
11. Dumbbell Charts


Most importantly, we will be using quandl api and quantmod api to import financial data in this notebook.
We can install quandl & quantmod packages and register an account on quandl.com

Create a free Quandl account and set your API key here

For more information, refer to :
https://www.quandl.com/tools/r

# Run the script below with your secret key obtained after registering on quandl
# quandl_sk = "<quandl secret key>"

# Alternative way to Load Data Tokens 
source("../R/Setup/apitokens.r")

# Data manipulation libraries used in this notebook
library(tibble) # includes rownames_to_column function
library(dplyr) # includes filter function
library(tidyr) # includes spread function

# Visualization libraries used in this notebook
library(ggplot2) # Includes ggplot function
library(ggforce) # Includes geom_sina & geom_parallel_sets
library(ggridges) # Includes geom_
library(cowplot) # Includes axis_canvas for generating A Canvas Onto Which One Can Draw Axis-Like Objects

# Data API libraries
library(Quandl) # To import data from Quandl API
library(quantmod) # To import data using yahoo,google api

# Animation Packages
library(gganimate) # to add animation to static plot 
library(gifski) # Converts images to GIF animations
library(png) # Provides an easy and simple way to read, write and display bitmap images stored in the PNG format.

# Datetime Parsing functions
library(lubridate) # includes datetime parsing functions

# Data visualization addon library for Themes and colors available in my github repository
# https://github.com/prabhupavitra/dviz.addon
library(dviz.addon)

# Resizing plot width and height from default 7
# https://www.rdocumentation.org/packages/repr/versions/0.7/topics/repr-options
options(repr.plot.width=10, repr.plot.height=4)

Importing Data (Using Quandl and quantmod API)

This notebook uses Quandl and quantmod API to import Financial Data directly to R.
Datasets used in this notebook include:

1. Historical Prices of Precious Metals 
    (Gold, Silver, Platinum and Palladium)
2. Historical Prices of Non-Ferrous Base Metals 
    (Aluminum; Copper; Lead; Nickel; Tin and Zinc)
3. Consumer Price Index for All Urban Consumers (CPI-U)

Precious Metals Prices : London Fixing (U.S. Dollars per Troy Ounce)

# Using Quandl API : Import prices of Precious Metals ( Gold, Silver, Platinum, Palladium )

# Gold Price: London Fixing
gold <- Quandl("LBMA/GOLD", api_key=quandl_sk)

# Silver Price: London Fixing
silver <- Quandl("LBMA/SILVER", api_key=quandl_sk)

# Platinum Fixing
platinum <- Quandl("LPPM/PLAT", api_key=quandl_sk)

# Palladium
palladium <- Quandl("LPPM/PALL", api_key=quandl_sk)

Base Metals Prices : Spot Prices (U.S. Dollars per metric ton)

# Other Base Metals (Aluminum; Cobalt; Copper; Iron Ore; Lead; Molybdenum; Nickel; Tin; Uranium and Zinc)

# Aluminum; 99.5% minimum purity; LME spot price; CIF UK ports; US$ per metric ton
aluminium <- Quandl("ODA/PALUM_USD", api_key=quandl_sk)

# Copper; grade A cathode; LME spot price; CIF European ports; US$ per metric ton
copper <- Quandl("ODA/PCOPP_USD", api_key=quandl_sk)

# Lead; 99.97% pure; LME spot price; CIF European Ports; US$ per metric ton
lead <- Quandl("ODA/PLEAD_USD", api_key=quandl_sk)

# Nickel; melting grade; LME spot price; CIF European ports; US$ per metric ton
nickel <- Quandl("ODA/PNICK_USD", api_key=quandl_sk)

# Tin; standard grade; LME spot price; US$ per metric ton
tin <- Quandl("ODA/PTIN_USD", api_key=quandl_sk)

# Zinc; high grade 98% pure; US$ per metric ton
zinc <- Quandl("ODA/PZINC_USD", api_key=quandl_sk)

CPI Data : CPI for U.S. City Average

Units:
Index 1982-1984=100,
Seasonally Adjusted

Frequency:
Monthly

# Consumer Price Index for All Urban Consumers: All Items in U.S. City Average 
monthly_allitems <- Quandl("FRED/CPIAUCSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average
monthly_foodbev <- Quandl("FRED/CPIFABSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average
monthly_excfoodnbev <- Quandl("FRED/CPILFESL", api_key=quandl_sk)  

# Consumer Price Index for All Urban Consumers: Recreation in U.S. City Average
monthly_recreation <- Quandl("FRED/CPIRECSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Housing in U.S. City Average 
monthly_housing <- Quandl("FRED/CPIHOSNS", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Education and Communication in U.S. City Average
monthly_eduncomm <- Quandl("FRED/CPIEDUSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Apparel in U.S. City Average
monthly_apparel <- Quandl("FRED/CPIAPPSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Energy in U.S. City Average
monthly_energy <- Quandl("FRED/CPIENGSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Other Goods and Services in U.S. City Average
monthly_othergns <- Quandl("FRED/CPIOGSSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Transportation in U.S. City Average 
monthly_trnsprt <- Quandl("FRED/CPITRNSL", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Services in U.S. City Average 
monthly_otherserv <- Quandl("FRED/CUSR0000SAS", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Commodities in U.S. City Average
monthly_commod <- Quandl("FRED/CUSR0000SAC", api_key=quandl_sk) 

# Consumer Price Index for All Urban Consumers: Medical Care in U.S. City Average
monthly_medcare <- Quandl("FRED/CPIMEDSL", api_key=quandl_sk)

Data Manipulation

Precious Metals Prices : London Fixing (U.S. Dollars per Troy Ounce)

# Merging the datasets of all 4 precious metals (Measured per Troy Ounce)
precious_metals <- 
rbind(gold %>% select("Date","USD (AM)") %>% rename(USD="USD (AM)") %>% mutate(Commodity="Gold"),
silver %>% select("Date","USD") %>% mutate(Commodity="Silver"),
platinum %>% select("Date","USD AM") %>% rename(USD="USD AM") %>% mutate(Commodity="Platinum"),
palladium %>% select("Date","USD AM") %>% rename(USD="USD AM") %>% mutate(Commodity="Palladium")) %>%
mutate(Year = year(Date)) %>% filter(USD!="NA")

head(precious_metals,5)

Date	USD	Commodity	Year
2020-08-28	1955.85	Gold	2020
2020-08-27	1938.80	Gold	2020
2020-08-26	1918.50	Gold	2020
2020-08-25	1925.45	Gold	2020
2020-08-24	1947.55	Gold	2020

Base Metals Prices : Spot Prices (U.S. Dollars per metric ton)

base_metals <-
rbind(aluminium %>% mutate(Commodity="Aluminium"),
      copper %>% mutate(Commodity="Copper"),
      lead %>% mutate(Commodity="Lead"),
      nickel %>% mutate(Commodity="Nickel"),
      tin %>% mutate(Commodity="Tin"),
      zinc %>% mutate(Commodity="Zinc")) %>%
mutate(Year = year(Date))

head(base_metals,1)

Date	Value	Commodity	Year
2020-07-31	1643.812	Aluminium	2020

CPI Data : CPI for U.S. City Average

cpi_monthly_sa <-
rbind(monthly_allitems %>% mutate(Category="CPI"),
      monthly_excfoodnbev %>% mutate(Category="Core CPI"),
      monthly_apparel %>% mutate(Category="Apparel"),
      monthly_commod %>% mutate(Category="Commodities"),
      monthly_eduncomm %>% mutate(Category="Education & Communication"),
      monthly_energy %>% mutate(Category="Energy"),
      monthly_foodbev %>% mutate(Category="Food & Beverages"),
      monthly_housing %>% mutate(Category="Housing"),
      monthly_medcare %>% mutate(Category="Medical Care"),
      monthly_othergns %>% mutate(Category="Other Goods & Services"),
      monthly_otherserv %>% mutate(Category="Other Services"),
      monthly_recreation %>% mutate(Category="Recreation"),
      monthly_trnsprt %>% mutate(Category="Transportation")) %>%
mutate (Year =year(Date),Month=month(Date))

head(cpi_monthly_sa,1)

Date	Value	Category	Year	Month
2020-07-01	258.723	CPI	2020	7

Some basic Math Calculations with CPI

# Percent Change (1982-Present)
cpi_pctchng <- filter(cpi_monthly_sa,#Month==1,
                                     Year>1982,
                                     !(Category %in% c('Energy','Transport'))) %>%
               group_by(Category) %>%
               arrange(Date) %>%
               mutate(PercentChange = (Value/lag(Value)-1)*100) %>%
               filter(PercentChange!="NA") 

# Over-the-year percent change 
cpi_otypct <- 
cpi_monthly_sa %>% 
filter(Year>1982,Month==12) %>% 
arrange(Date) %>% 
group_by(Category) %>% 
mutate(OverYrPercentChange = (Value/lag(Value)-1)*100) %>%
filter(OverYrPercentChange!="NA")


# Annual Average, Annual Average CHange and Purchasing Power
cpi_annualavg <-
cpi_monthly_sa %>% filter(Year>1982,Year<2020) %>%
arrange(Date) %>%
group_by(Year,Category) %>%
summarise(AnnualAvg = mean(Value)) %>%
group_by(Category) %>%
mutate(AnnualAvgChange = (AnnualAvg/lag(AnnualAvg)-1)*100,
       PurchasingPower = (AnnualAvg/lag(AnnualAvg)*100)) %>%
filter(AnnualAvgChange!="NA")

Visualizing Amounts

Bar Chart

A simple way to visualize the quantitative data of a categorical variable is the bar chart. The height of
the bar is proportional to the quantity we want to visualize. They can be used for displaying data that are 
classified as nominal data/ordinal data.

They are one of the most commonly used types of plots because they are simple to create and very easy to 
interpret. They are also a flexible chart type and there are several variations of the standard bar chart.

Different types of bar charts:
- Horizontal bar charts
- Grouped or Component charts
- Stacked bar charts
- Diverging bar charts
- Animated bar charts

# Resizing the plot 
options(repr.plot.width=10, repr.plot.height=6)

# Subset of Consumer Price Index for All Urban Consumers for the year 2018
df_barplot_categories <- filter(cpi_annualavg,
                                Year==2018,
                                !Category %in% c('CPI','Core CPI')) %>%
                         mutate(plotorder = 1)

df_barplot_all <- filter(cpi_annualavg,
                         Year==2018,
                         Category %in% c('CPI','Core CPI')) %>% 
                  mutate(plotorder = 0)
                
# Plotting the bar plot
barplot_cpi <-
 ggplot(size=20) +
 geom_bar(data=df_barplot_categories, 
           aes(x=reorder(Category,AnnualAvgChange),
           y=AnnualAvgChange),
          stat="identity", 
          width= 0.7, 
          fill="steelblue") +
 geom_bar(data = df_barplot_all,
            aes(x = reorder(Category,plotorder),y=AnnualAvgChange),
            stat="identity", 
            width=0.125, 
            fill="steelblue") +
 geom_text(data=df_barplot_categories, 
           aes(x=reorder(Category,AnnualAvgChange),
               y=(AnnualAvgChange+ 0.25 * sign(AnnualAvgChange)),
           label =paste(" ",round(AnnualAvgChange,2))),
           hjust=0.5) +
 geom_text(data=df_barplot_all,
           aes(x = reorder(Category,plotorder),
               y= (AnnualAvgChange+ 0.25 *sign(AnnualAvgChange)),
           label =paste(" ",round(AnnualAvgChange,2))),
           hjust=0.5) +
 theme_pprabhu_grid() + 
 labs(title = "Consumer Price Index for All Urban Consumers",
      subtitle="(Year 2018)",
      x="",
      y="Compounded Annual Rate of Change") +
 coord_flip() +
 theme(plot.title = element_text(hjust = 0.5),
       plot.subtitle = element_text(hjust = 0.5,size=14),
       axis.text.x = element_text(angle=0,hjust = 1),
       strip.text.x = element_blank())  +
 facet_wrap(.~plotorder, nrow = 2,scales="free_y",shrink = FALSE)  
 

barplot_cpi

Diverging Bar Chart

A diverging bar chart is a bar chart with a diverging aspect; i.e. It makes comparison of amounts from a 
divergent line easy to visualize. This divergent line can represent zero, but it can also be used to simply 
separate the two distinguishing members of the dataset based on the amount.

In the plot depicted below, the divergent line is zero; Its quite easier to visualize which Categories saw a 
positive rate of inflation vs negative.

# Diverging bar plot 
cpi_annual <- cpi_annualavg %>% filter(Year==2019)

# Plotting the diverging bar plot
barplot_cpiannual <-
ggplot(cpi_annual, 
       aes(x=reorder(Category,AnnualAvgChange),
           y=AnnualAvgChange,
           fill = AnnualAvgChange>0))+
  geom_bar(stat="identity")+
  geom_text(aes(x=reorder(Category,AnnualAvgChange),
                y=(AnnualAvgChange+ 0.28 * sign(AnnualAvgChange)),
            label =paste(" ",round(AnnualAvgChange,2))),
            hjust=0.5) +
  labs(x="",
       y="Average Annual Inflation",
       title ="CPI Annual Average Change",
       subtitle="(Year 2019)") +
  theme_pprabhu_grid() +
  coord_flip() +
  theme(plot.subtitle = element_text(size=14),
        axis.text.x = element_text(angle = 0, hjust = 1)) +
  guides(fill = FALSE)
 
barplot_cpiannual

Stacked Bar Chart

The stacked bar chart extends the standard bar chart from visualizing quantity of one categorical variable 
to two. Each bar in a standard bar chart is divided into a number of sub-bars stacked end to end, each 
one corresponding to a level of the second categorical variable. The main objective of a standard bar chart 
is to compare numeric values between levels of a categorical variable. It also visualizes relative 
decomposition of each of the categories.

The stacked bar chart below depicts historical annual prices of precious metals. The two categorical 
variables: Commodity and Year. The primary categorical variable is the Commodity. It also depicts the 
relative decomposition of each of the commodities. For example, we can compare prices of 2018 vs 2019 in 
the stacked bar chart and infer that although the prices of gold, platinum and silver were steady, the 
palladium prices have risen considerably.

# Annual Metal prices (Includes Gold, Silver, Platinum and Palladium)
metals_stacked <-  filter(precious_metals,!is.na(USD),Year<2020,Year>2000) %>%
                   group_by(Year,Commodity) %>%
                   summarise(AnnualPrice = round(mean(USD),2))   

# Plot the stacked barplot using geom_bar()
metals_stackedplot <- 
ggplot(data=metals_stacked, 
       aes(x=factor(Year),
           y=AnnualPrice,
           fill=Commodity)) +
       geom_bar(stat="identity",
                color="white",
                alpha=0.9) +
       labs(x="",y="Average Annual Prices in USD") +
       ggtitle("Metals Prices History(2001-2019)") +
       theme_pprabhu() +
       theme(plot.title = element_text(hjust = 0.5),
            axis.text.x = element_text(hjust = 1)) +
       scale_fill_pprabhu(palette = "sseq",discrete=TRUE) 

metals_stackedplot

Grouped Bar Chart

The grouped bar chart, also known as clustered bar graph, multi-set bar chart, or grouped column chart is a 
variation of bar chart used when we need to visualize distribution of data points or making comparisons 
across different categories of data.

They are used when you want to do within-group and between-group comparisons in one single plot. For 
example, in the plot below, we can check the price comparisons of base metals within each year as well as 
over a period of time.

# Grouped Bar plot using geom_bar()
metals_grouped <-  filter(base_metals,!is.na(Value),Year<2020,Year>2000) %>%
                   group_by(Year,Commodity) %>%
                   summarise(AnnualPrice = round(mean(Value),2))   

metals_groupedplot <-
ggplot(data=metals_grouped, aes(x=factor(Year),
                                y=AnnualPrice,
                                fill=Commodity)) +
       geom_bar(stat="identity", 
                position=position_dodge(),
                alpha=0.8,
                color="white") +
       labs(x="",y="Average Annual Prices in USD") +
       ggtitle("Price History of Base Metals (2001-2019)") +
       scale_fill_pprabhu(palette = "qual") +
       theme_pprabhu() + 
       theme(plot.title = element_text(hjust = 0.5))

metals_groupedplot

Animated Bar Chart

Animated bar charts are mostly used for visualization of trends over time. They provide a holistic data 
story/insight in a concise and easy to understand chart, which makes them more popular in social media.

# Creating a Animated Bar Chart using ggplot and geom_tile
cpi_input_format <-
cpi_annualavg %>% 
group_by(Year)%>%      
mutate(rank = rank(-AnnualAvgChange),
       Value_rel = AnnualAvgChange/AnnualAvgChange[rank==1],
       Value_lbl = paste0(" ",round(AnnualAvgChange,2))) %>%
group_by(Category) %>% 
filter(rank <= 10)

# Creates images for every year
anim <- 
ggplot(cpi_input_format, 
       aes(rank, group = Category))+
  geom_tile(aes(y = AnnualAvgChange/2,
                height = AnnualAvgChange,
                fill = AnnualAvgChange>0,
                width = 0.9), alpha = 0.8) +
  geom_text(aes(y =sign(AnnualAvgChange)-4, 
                label = paste(Category, " ")), 
            vjust = 0.2, 
            hjust = 1,
            size=7) + 
  geom_text(aes(y=(AnnualAvgChange+ 0.45 * sign(AnnualAvgChange)),
            label =paste(" ",round(AnnualAvgChange,2))),
            hjust=0.5,
            size=7) +
  coord_flip(clip = "off", 
             expand = TRUE) +
  scale_x_reverse() +
  theme_pprabhu() +
  theme(legend.position="none",
        plot.margin = margin(1,4, 1, 8, "cm"),
        axis.line=element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.title.x=element_blank(),
        axis.title.y=element_blank()) +
  transition_states(Year, transition_length = 4, state_length = 1) +
  ease_aes('cubic-in-out') + # For a smoother look
  labs(title = 'CPI Annual Average by Category: {closest_state}')

# Stores the gif_image object in the working directory
animate(anim, nframes = 350, 
              fps = 15,
              width = 1200, 
              height = 1000, 
              renderer = gifski_renderer("gganim.gif"))

# This line inserts the image inline
IRdisplay::display_png(file = "gganim.gif")

Dot Plots

Dot plots are one of the simplest way to visualize single numerical variable with a modest number of 
observations. They are a great alternative to the bar chart and the power of these plots becomes evident 
on refining and they can easily communicate important aspects of your data to viewers. 

In the example below, we have considered depicting Gold fixing price. Although this is a simple and basic 
case of dot plot, they are capable of depicting lot more information. In the latter example, we use dot plot 
to visualize the rate of inflation from 2018 to 2019 and highlight the categories which underwent more than 
20% rise in inflation rate.

dotplot_data <- filter(precious_metals,Commodity=="Gold",!is.na(USD),
                       Year<2020,
                       Year>2000) %>%
                group_by(Year) %>%
                summarise(AnnualPrice = mean(USD))

# Dot plot using geom_point()
gold_dotplot <-
ggplot(data=dotplot_data, 
       aes(x = as.character(Year),y=AnnualPrice)) +
       geom_point(color = "#0072B2", size = 3) +
       labs(x="Year",y="U.S. Dollars per Troy Ounce") +
       ggtitle("Dot plot of Gold Fixing Price 10:30 A.M.(London time) in London Bullion Market") +
       theme_pprabhu_hgrid() + 
       theme(plot.title = element_text(hjust = 0.5),
             axis.text.x = element_text(angle=0)) + 
       coord_flip()       

gold_dotplot

Dot Plot

# Creating a data frame that identifies inflation changes over 20%
cpi_diff <- filter(cpi_annualavg,Year %in% c('2018','2019')) %>% 
            select(Year,Category,AnnualAvgChange) %>%
            spread(Year,AnnualAvgChange) %>%
            rename(Year_2019=3,Year_2018=2) %>%
            group_by(Category) %>%
            mutate(Diff = Year_2019 / Year_2018 - 1) %>% 
            arrange(desc(Diff)) %>%
            filter(Diff > .2)

# Creating a data frame for labels
labels_df <- filter(cpi_annualavg,Year %in% c('2018','2019')) %>% 
             select(Year,Category,AnnualAvgChange) %>%
             spread(Year,AnnualAvgChange) %>%
             rename(Year_2019=3,Year_2018=2) %>%
             group_by(Category) %>%
             mutate(left_label = min(Year_2018,Year_2019),
                    right_label = max(Year_2018,Year_2019)) %>%
             mutate(left_Year=if_else(left_label==Year_2018,"2018","2019"),
                    right_Year=if_else(right_label==Year_2018,"2018","2019"))

left_labels <- select(labels_df,"Category","left_label","left_Year")
right_labels <- select(labels_df,"Category","right_label","right_Year")

# Creating the main data frame for dot plot
df_cpi <- select(labels_df,"Category","Year_2018","Year_2019") %>% 
         gather(Year,AnnualAvgChange,-Category)

# Filtering main data frame to including Categories where the inflation changes exceeds 20%.
highlight <- filter(df_cpi,Category %in% cpi_diff$Category)

# Plots dot plot showing changes in CPI from 2018-2019 in all major categories
cpi_dualdotplot <-
ggplot(df_cpi,aes(x = df_cpi$AnnualAvgChange,y=df_cpi$Category)) +
        geom_line(aes(group = Category), alpha = .3) +
        geom_point(aes(color = Year), size = 1.5, alpha = .3) +
        geom_line(data = highlight,
                  aes(x = highlight$AnnualAvgChange,
                      y=highlight$Category,
                      group = Category)) +
        geom_point(data = highlight, 
                   aes(x = highlight$AnnualAvgChange,
                       y=highlight$Category,
                       color = Year), 
                   size = 2) +
        geom_text(data=left_labels,
                  aes(x= left_labels$left_label,
                      y= left_labels$Category,
                      label = round(left_labels$left_label, 2)),
                  color=left_labels$left_Year,
                  alpha= if_else(right_labels$Category %in% highlight$Category,1,0.3),
                  size = 3, 
                  hjust = 1.5) +
        geom_text(data=right_labels,
                  aes(x= right_labels$right_label,
                      y= right_labels$Category,
                      label = round(right_labels$right_label, 2)),
                  color=right_labels$right_Year,
                  size = 3,
                  alpha = if_else(right_labels$Category %in% highlight$Category,1,0.3),
                  hjust = -.4) +
        theme_pprabhu_grid() +
        scale_colour_manual(
          values = c("red","green"),
          breaks = c("Year_2018", "Year_2019"),
          labels = c("2018", "2019")) +
        labs(title="Inflation Changes in major Categories", 
             subtitle="(2018-2019)",
             x="Change in Inflation Annual Average",
             y="") +
        theme(legend.position="right",
              legend.justification = c("right", "bottom"),
              legend.margin = margin(5, 5, 5, 5),
              axis.text.x = element_text(angle=0),
              plot.title=element_text(hjust=0.5,face = "italic"),
              plot.subtitle=element_text(hjust=0.5)) +
        xlim(-3,7.5)

cpi_dualdotplot

Bubblechart

A bubble chart uses areas of circle to represent the quantity of a numeric variable. It is a preferred when 
we want to represent relationship between two or more numeric variables.

In the following example, we represent the prices of base metals for the year 2019.

bubble_df <- base_metals %>% 
             filter(base_metals$Year==2019) %>% 
             group_by(Commodity) %>%
             summarise(AnnualPrice = round(mean(Value),2))   

basemetals_bubbleplot <-
ggplot(bubble_df,aes(x=AnnualPrice,
                     y=Commodity,
                     color = Commodity,
                     size=AnnualPrice)) +
    geom_point(aes(size=AnnualPrice),alpha=0.4) +
    scale_size(range = c(6, 24), 
               name="Annual Price") +
    geom_text(aes(x=AnnualPrice+1500,
                  y = Commodity,
                  label=paste("\n",Commodity,"\n","(",AnnualPrice,")")),
                  size=3) +
    labs(title="Price comparison of base metals", 
             subtitle="(Year 2019)",
             x="U.S. Dollars per metric ton",
             y="") +
    xlim(0,21000)+
    theme_pprabhu_vgrid() +
    guides(color=FALSE,size=FALSE) +
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.text.x = element_text(angle=0),
          plot.title = element_text(hjust=0.5),
          plot.subtitle = element_text(hjust=0.5))

basemetals_bubbleplot

Heatmap

A heatmap depicts values for a main variable of interest across two axis variables as a grid and uses color 
coding to represent the quantity of values.

In the below example, we generate a heatmap to depict historic monthly change in rate of inflation for major 
categories.

# Plots the heatmap on the CPI monthly Percent Change data
heatmap_plot <-
ggplot(data=filter(cpi_annualavg,Year>1995),
       aes(x=Year,
           y=Category,
           fill=scale(AnnualAvgChange))) +
       geom_tile(size=0.35,
                 aes(fill = scale(AnnualAvgChange))) +
       labs(x="",
            y="",
            fill = "Annual Average Change") +
       ggtitle("CPI Inflation Heatmap (Annual Average)") +
       scale_x_discrete(expand = c(0, 0)) +
       scale_y_discrete(expand = c(0, 0)) +
       theme_pprabhu() + 
       scale_fill_pprabhu(palette = "sseq",
                          discrete=FALSE,
                          reverse = FALSE) +
       theme(plot.title = element_text(hjust = 0.5,vjust=-0.5),
             axis.text.x = element_text(angle=0),
             legend.position="bottom", 
             legend.direction = "horizontal")


heatmap_plot